{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./winequality-red.csv', sep=';')\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Ip2FJwOyIeERSB3AT8IcRsa3eMj09PdHX15e5jy0793H+NbvoHxgcLuvsKHHx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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49911ebd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "\n",
    "plt.scatter(df['alcohol'], df['quality'])\n",
    "plt.xlabel('Alcohol')\n",
    "plt.ylabel('Quality')\n",
    "plt.title('Alcohol Against Quality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
